On the basis of gene expression profiling, the laboratory proposed that the most common form of lymphoma, diffuse large B cell lymphoma (DLBCL), is a composite of three molecularly distinct diseases that are indistinguishable by standard diagnostic methods. These diseases, termed germinal center B cell-like (GCB) DLBCL, activated B cell-like (ABC) DLBCL, and primary mediastinal B cell lymphoma (PMBL), arise from B lymphocytes at different stages of differentiation by distinct oncogenic pathways. The curative response of patients with DLBCL to chemotherapy is highly variable, and the DLBCL subtype distinction accounts, in part, for this heterogeneity. With CHOP multi-agent chemotherapy, the 5-year survival rates of ABC DLBCL and GCB DLBCL are 60% and 30%, respectively. This clinical disparity likely reflects the host of genetic differences between these DLBCL subtypes. A recurring theme that emerges from our molecular profiling efforts in lymphoma is that the curative response to treatment and the length of survival following diagnosis are dictated by molecular features of the tumors at diagnosis. In DLBCL, we developed a multivariate model of therapeutic outcome based on gene expression signatures, which quantitatively reflected distinct aspects of tumor biology. This study was performed in the era of CHOP chemotherapy for DLBCL, which has subsequently been supplanted by regimens that add the anti-CD20 monoclonal antibody Rituximab (R-CHOP). We therefore created a new gene expression model that was strongly associated with both overall and progression-free survival in the CHOP and R-CHOP cohorts as well as in a third CHOP cohort. The gene expression-based survival model could divide the patients in the R-CHOP cohort into quartile groups that had 3-year progression-free survival rates of 84%, 69%, 61% and 34%, respectively, indicating that the model captures much of the heterogeneity in therapeutic response. The germinal center B cell signature mirrored the distinction between GCB and ABC DLBCL and therefore reflects the myriad genetic and epigenetic differences that exist between these two subtypes. On the other hand, the stromal-1 and stromal-2 signatures reflected different aspects of the tumor microenvironment. The stromal-1 signature, which was associated with favorable outcome, identified tumors that were fibrotic and rich in histiocytic cells of the myeloid lineage. The stromal-2 signature, which was associated with poor outcome, included a host of genes that are characteristically expressed in endothelial cells and was correlated with increased tumor blood vessel density, revealing an unanticipated role for angiogenesis in DLBCL. Array-based comparative genomic hybridization was used to identify genomic changes in copy number that influenced survival. Two genomic alterations that occurred exclusively in ABC DLBCL were deletion of the INK4a/ARF tumor suppressor locus and trisomy 3. These genetic aberrations, considered separately and together, identified a subset of patients with ABC DLBCL with inferior prognosis relative to other patients with this DLBCL subtype. This ABC DLBCL subset was also characterized by oncogenic mutations that activate various survival signaling pathways. These include mutations in the CARD11 gene, which encodes a scaffold molecule required for NF-kB signaling downstream of the B cell receptor. Mutations in the B cell receptor components CD79A and CD79B potentiate ongoing B cell receptor signaling. Mutations in the signaling adapter MYD88 create spontaneously active isoforms. Each of these mutations contribute to constitutive NF-kB activity in ABC DLBCLs in which they have been acquired. We are currently investigating several platforms to deliver the molecular diagnostic and prognostic distinction to patients with lymphoma. The goal is to utilize formalin-fixed and paraffin-embedded tissue for these analyses since most lymphoma biopsies are routinely stored in this fashion. The Nanostring platform for digital gene expression analysis has proved highly effective in distinguishing ABC and GCB DLBCL. This technology has been licensed by Nanostring and is currently being used to develop a companion diagnostic for the use of lenalidomide to treat ABC DLBCL. Most recently, we have been conducting genomic analysis of patients enrolled in therapeutic clinical trials. In a phase 2 trial of ibrutinib in relapsed/refractory DLBCL, we used gene expression profiling to subdivide the cases into ABC and GCB subtypes. The response rate in ABC DLBCL was significantly greater than in GCB DLBCL (37% vs. 5%), as predicted by our laboratory studies showing addiction to chronic active B cell receptor (BCR) signaling and ibrutinib sensitivity in cell line models of ABC DLBCL. Analysis of recurrent mutations in ABC DLBCL revealed a higher response rate in tumors with mutations affecting the BCR subunit CD79B and especially in tumors with both CD79B and MYD88 mutations. This suggests that ABC DLBCL can be usefully subdivided further based on genetic abnormalities in order to predict response to targeted agents. To test this, we undertook a multi-platform genomic analysis of 574 DLBCL tumors and integrated gene expression profiling with analysis of DNA copy number alterations, translocations, and mutations, resulting in a genetic taxonomy of DLBCL that has provided unexpected biological and clinical insights1. Tumors were classified into the same genetic subtype if they shared multiple recurrent genetic alterations. This approach yielded four DLBCL genetic subtypes - termed MCD, BN2, N1, and EZB - that refine and extend the gene expression-based classification of DLBCL1. MCD and N1 tumors are primarily subsets of ABC DLBCL and EZB is a subset of GCB DLBCL, but BN2 tumors are drawn from ABC, GCB and Unclassified DLBCL. Strong support for the biological and clinical relevance of the DLBCL genetic subtypes came from analysis of responses to immunochemotherapy1. The MCD and N1 subtypes had a substantially better overall survival than the BN2 and EZB genetic subtypes. The 5-year survival fractions in MCD, N1, BN2 and EZB DLBCL were 26%, 36%, 65%, 68%, respectively. Within ABC DLBCL, the survival of MCD and N1 patients was inferior to the survival of BN2 patients. Within GCB DLBCL, the survival of EZB patients was inferior to other GCB patients. Thus, the prognostic information provided by the genetic subtype distinction extends and improves upon the prognostic differences provided by gene expression profiling. One implication of these findings is that patients with MCD DLBCL should consider clinical trials of a novel agent(s) combined with immunochemotherapy, given the relative low 5-year survival in this group following chemotherapy alone. A second implication is that clinical trials in which R-CHOP is combined with a novel agent should ascertain which DLBCL genetic subtypes were enrolled, given the striking differences in outcome following R-CHOP alone among these subtypes. Current efforts aim to develp a clinically feasible and useful tool to classify DLBCL tumors into genetic subtypes and to deploy these methods in precision medicine clinical trials.